scholarly journals Hydro-NEXRAD-2: real-time access to customized radar-rainfall for hydrologic applications

2012 ◽  
Vol 15 (2) ◽  
pp. 580-590 ◽  
Author(s):  
Witold F. Krajewski ◽  
Anton Kruger ◽  
Satpreet Singh ◽  
Bong-Chul Seo ◽  
James A. Smith
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Rain Rate ◽  
Time Synchronization ◽  
Handling Time ◽  
Radar Data ◽  
Prototype System ◽  
Radar Rainfall ◽  
Estimation Algorithms ◽  
Wide Range

Hydro-NEXRAD-2 (HNX2) is a prototype system that allows hydrologic users real-time access to NEXRAD radar data in support of a wide range of research. The system processes basic radar data (Level II) and delivers radar-rainfall products based on the user's custom selection of features such as spatial domain, rainfall product space and time resolution, and rainfall estimation algorithms. HNX2 collects real-time, unprocessed data from multiple NEXRAD radars as they become available, processes them through a user-configurable pipeline of data-processing modules, and publishes the processed data-products at regular intervals. Modules in the data-processing pipeline encapsulate algorithms such as non-meteorological echo detection, radar range correction, radar-reflectivity-rain rate (Z-R) conversion, echo advection correction, mosaicking of products from multiple radars, and grid projections and transformations. This paper describes the challenges involved in HNX2's development and implementation, which include real-time error-handling, time-synchronization of data from multiple asynchronous sources, generation of multiple-radar metadata products, and distribution of products to a user base with diverse needs and constraints. HNX2 publishes products through automation and allows multiple users access to published products. Currently, HNX2 is serving near real-time rain-rate maps for Iowa in the USA using data from seven radars covering the state. Hydrologic models operated by The University of Iowa's Iowa Flood Center use these products.

scholarly journals TreeNet–The Biological Drought and Growth Indicator Network

2021 ◽  
Vol 4 ◽  
Author(s):  
Roman Zweifel ◽  
Sophia Etzold ◽  
David Basler ◽  
Reinhard Bischoff ◽  
Sabine Braun ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Environmental Conditions ◽  
Forest Growth ◽  
Water Dynamics ◽  
Automated System ◽  
Indicator Network ◽  
High Temporal Resolution ◽  
Stem Size ◽  
Wide Range

The TreeNet research and monitoring network has been continuously collecting data from point dendrometers and air and soil microclimate using an automated system since 2011. The goal of TreeNet is to generate high temporal resolution datasets of tree growth and tree water dynamics for research and to provide near real-time indicators of forest growth performance and drought stress to a wide audience. This paper explains the key working steps from the installation of sensors in the field to data acquisition, data transmission, data processing, and online visualization. Moreover, we discuss the underlying premises to convert dynamic stem size changes into relevant biological information. Every 10 min, the stem radii of about 420 trees from 13 species at 61 sites in Switzerland are measured electronically with micrometer precision, in parallel with the environmental conditions above and below ground. The data are automatically transmitted, processed and stored on a central server. Automated data processing (R-based functions) includes screening of outliers, interpolation of data gaps, and extraction of radial stem growth and water deficit for each tree. These long-term data are used for scientific investigations as well as to calculate and display daily indicators of growth trends and drought levels in Switzerland based on historical and current data. The current collection of over 100 million data points forms the basis for identifying dynamics of tree-, site- and species-specific processes along environmental gradients. TreeNet is one of the few forest networks capable of tracking the diurnal and seasonal cycles of tree physiology in near real-time, covering a wide range of temperate forest species and their respective environmental conditions.

A Real-Time, Three-Dimensional, Rapidly Updating, Heterogeneous Radar Merger Technique for Reflectivity, Velocity, and Derived Products

2006 ◽  
Vol 21 (5) ◽  
pp. 802-823 ◽  
Author(s):  
Valliappa Lakshmanan ◽  
Travis Smith ◽  
Kurt Hondl ◽  
Gregory J. Stumpf ◽  
Arthur Witt
Keyword(s):  
Real Time ◽  
Time Synchronization ◽  
Three Dimensional ◽  
Radar Data ◽  
Streaming Data ◽  
Weather Radars ◽  
Beam Geometry ◽  
Time Stamps ◽  
Scalar Products ◽  
Radar Calibration

Abstract With the advent of real-time streaming data from various radar networks, including most Weather Surveillance Radars-1988 Doppler and several Terminal Doppler Weather Radars, it is now possible to combine data in real time to form 3D multiple-radar grids. Herein, a technique for taking the base radar data (reflectivity and radial velocity) and derived products from multiple radars and combining them in real time into a rapidly updating 3D merged grid is described. An estimate of that radar product combined from all the different radars can be extracted from the 3D grid at any time. This is accomplished through a formulation that accounts for the varying radar beam geometry with range, vertical gaps between radar scans, the lack of time synchronization between radars, storm movement, varying beam resolutions between different types of radars, beam blockage due to terrain, differing radar calibration, and inaccurate time stamps on radar data. Techniques for merging scalar products like reflectivity, and innovative, real-time techniques for combining velocity and velocity-derived products are demonstrated. Precomputation techniques that can be utilized to perform the merger in real time and derived products that can be computed from these three-dimensional merger grids are described.

scholarly journals Influence of the Subgrid Variability of the Raindrop Size Distribution on Radar Rainfall Estimators

2012 ◽  
Vol 51 (4) ◽  
pp. 780-785 ◽  
Author(s):  
Joël Jaffrain ◽  
Alexis Berne
Keyword(s):  
Size Distribution ◽  
Rain Rate ◽  
Power Laws ◽  
Radar Data ◽  
Sampling Uncertainty ◽  
Radar Rainfall ◽  
Differential Phase Shift ◽  
Raindrop Size Distribution ◽  
Raindrop Size ◽  
Pixel Scale

AbstractThis work aims at quantifying the variability of the parameters of the power laws used for rain-rate estimation from radar data, on the basis of raindrop size distribution measurements over a typical weather radar pixel. Power laws between the rain rate and the reflectivity or the specific differential phase shift are fitted to the measured values, and the variability of the parameters is analyzed. At the point scale, the variability within this radar pixel cannot be solely explained by the sampling uncertainty associated with disdrometer measurements. When parameters derived from point measurements are applied at the radar pixel scale, the resulting error in the rain amount varies between −2% and +15%.

scholarly journals PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation

2020 ◽  
Vol 101 (3) ◽  
pp. E286-E302 ◽  
Author(s):  
Phu Nguyen ◽  
Eric J. Shearer ◽  
Mohammed Ombadi ◽  
Vesta Afzali Gorooh ◽  
Kuolin Hsu ◽  
...  
Keyword(s):  
Real Time ◽  
Rain Rate ◽  
Extreme Rainfall ◽  
Rate Model ◽  
Spatiotemporal Resolution ◽  
Atmospheric Rivers ◽  
Estimation Algorithms ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Products Analysis

Abstract Precipitation measurements with high spatiotemporal resolution are a vital input for hydrometeorological and water resources studies; decision-making in disaster management; and weather, climate, and hydrological forecasting. Moreover, real-time precipitation estimation with high precision is pivotal for the monitoring and managing of catastrophic hydroclimate disasters such as flash floods, which frequently transpire after extreme rainfall. While algorithms that exclusively use satellite infrared data as input are attractive owing to their rich spatiotemporal resolution and near-instantaneous availability, their sole reliance on cloud-top brightness temperature (Tb) readings causes underestimates in wet regions and overestimates in dry regions—this is especially evident over the western contiguous United States (CONUS). We introduce an algorithm, the Precipitation Estimations from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain rate model (PDIR), which utilizes climatological data to construct a dynamic (i.e., laterally shifting) Tb–rain rate relationship that has several notable advantages over other quantitative precipitation-estimation algorithms and noteworthy skill over the western CONUS. Validation of PDIR over the western CONUS shows a promising degree of skill, notably at the annual scale, where it performs well in comparison to other satellite-based products. Analysis of two extreme landfalling atmospheric rivers show that solely IR-based PDIR performs reasonably well compared to other IR- and PMW-based satellite rainfall products, marking its potential to be effective in real-time monitoring of extreme storms. This research suggests that IR-based algorithms that contain the spatiotemporal richness and near-instantaneous availability needed for rapid natural hazards response may soon contain the skill needed for hydrologic and water resource applications.

Comparison of various X-band radar products over the Paris area

2020 ◽  
Author(s):  
Wiam Salih ◽  
Auguste Gires ◽  
Ioulia Tchiguirinskaia ◽  
Daniel Schertzer
Keyword(s):  
Power Law ◽  
Rain Rate ◽  
Dynamic Models ◽  
Weather Conditions ◽  
Radar Data ◽  
Morphological Comparison ◽  
Differential Phase Shift ◽  
X Band ◽  
Wide Range ◽  
Paris Area

<p>Optimized management of storm water management in the Paris area is needed to both avoid urban flooding and maximize water depollution. Such management requires improving the ability to measure and model hydro-meteorological events at the highest possible resolution. Hence, the interest of meteorological radars, given their unique ability to measure rainfall in both space and time.</p><p>In this study, we focus on the data collected by a dual polarimetric X-band radar data operated by Ecole des Ponts ParisTech in the framework of the Fresnel Platform is used. The space resolution is of 250 m and the time one is of 3 min and 25 seconds. Seven rainfall events that occurred in 2018 are studied. They cover a wide range of meteorological situations, including hail. More precisely several products are compared; some relying on a simple Marshall Palmer power law relation between the measured reflectivity and the rain rate; and others using the dual polarization capabilities for heavy rainfall through a power law relation between the measured specific differential phase shift and the rain rate. Constant and varying parameters for these laws are tested. In addition, these radar products are compared with various products obtained with a C-band radar operated by Meteo-France and 8 rain gauges. Temporal evolutions of rain rates are compared and classical metrics (Nash Sutcliff, correlation…) are computed. In addition, outputs of hydro-dynamic models’ simulations using this rainfall data are compared.</p><p>It appears that the results strongly depend on rainfall event, and even given peaks, with no clear tendency between the radar products. In addition, a strong dependency on the radar data processing, and especially the coefficients of the radar relation, is found. This suggests that further work should be done to improve their determination for this area and depending on the weather conditions. In addition, this study highlights the need to develop morphological comparison techniques that would be valid not only at a single scale but across scales.</p><p>Authors greatly acknowledge support of the chair Hydrology for Chair of Hydrology for Resilient Cities (endowed by Veolia) of the Ecole des Ponts ParisTech.</p><p> </p>

Improved radar data processing algorithms for quantitative rainfall estimation in real time

2009 ◽  
Vol 60 (1) ◽  
pp. 175-184 ◽  
Author(s):  
S. Krämer ◽  
H.-R. Verworn
Keyword(s):  
Real Time ◽  
Radar Data ◽  
Real Time Control ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Microphysical Properties ◽  
Time Control ◽  
Reflectivity Data ◽  
Radar Data Processing

This paper describes a new methodology to process C-band radar data for direct use as rainfall input to hydrologic and hydrodynamic models and in real time control of urban drainage systems. In contrast to the adjustment of radar data with the help of rain gauges, the new approach accounts for the microphysical properties of current rainfall. In a first step radar data are corrected for attenuation. This phenomenon has been identified as the main cause for the general underestimation of radar rainfall. Systematic variation of the attenuation coefficients within predefined bounds allows robust reflectivity profiling. Secondly, event specific R–Z relations are applied to the corrected radar reflectivity data in order to generate quantitative reliable radar rainfall estimates. The results of the methodology are validated by a network of 37 rain gauges located in the Emscher and Lippe river basins. Finally, the relevance of the correction methodology for radar rainfall forecasts is demonstrated. It has become clearly obvious, that the new methodology significantly improves the radar rainfall estimation and rainfall forecasts. The algorithms are applicable in real time.

scholarly journals Bollène-2002 Experiment: Radar Quantitative Precipitation Estimation in the Cévennes–Vivarais Region, France

2009 ◽  
Vol 48 (7) ◽  
pp. 1422-1447 ◽  
Author(s):  
Guy Delrieu ◽  
Brice Boudevillain ◽  
John Nicol ◽  
Benoît Chapon ◽  
Pierre-Emmanuel Kirstetter ◽  
...  
Keyword(s):  
Data Processing ◽  
Processing System ◽  
Ground Level ◽  
Radar Data ◽  
Rain Gauge ◽  
Space Time ◽  
Time Step ◽  
Radar Rainfall ◽  
Mountainous Regions ◽  
Study Results

Abstract The Bollène-2002 Experiment was aimed at developing the use of a radar volume-scanning strategy for conducting radar rainfall estimations in the mountainous regions of France. A developmental radar processing system, called Traitements Régionalisés et Adaptatifs de Données Radar pour l’Hydrologie (Regionalized and Adaptive Radar Data Processing for Hydrological Applications), has been built and several algorithms were specifically produced as part of this project. These algorithms include 1) a clutter identification technique based on the pulse-to-pulse variability of reflectivity Z for noncoherent radar, 2) a coupled procedure for determining a rain partition between convective and widespread rainfall R and the associated normalized vertical profiles of reflectivity, and 3) a method for calculating reflectivity at ground level from reflectivities measured aloft. Several radar processing strategies, including nonadaptive, time-adaptive, and space–time-adaptive variants, have been implemented to assess the performance of these new algorithms. Reference rainfall data were derived from a careful analysis of rain gauge datasets furnished by the Cévennes–Vivarais Mediterranean Hydrometeorological Observatory. The assessment criteria for five intense and long-lasting Mediterranean rain events have proven that good quantitative precipitation estimates can be obtained from radar data alone within 100-km range by using well-sited, well-maintained radar systems and sophisticated, physically based data-processing systems. The basic requirements entail performing accurate electronic calibration and stability verification, determining the radar detection domain, achieving efficient clutter elimination, and capturing the vertical structure(s) of reflectivity for the target event. Radar performance was shown to depend on type of rainfall, with better results obtained with deep convective rain systems (Nash coefficients of roughly 0.90 for point radar–rain gauge comparisons at the event time step), as opposed to shallow convective and frontal rain systems (Nash coefficients in the 0.6–0.8 range). In comparison with time-adaptive strategies, the space–time-adaptive strategy yields a very significant reduction in the radar–rain gauge bias while the level of scatter remains basically unchanged. Because the Z–R relationships have not been optimized in this study, results are attributed to an improved processing of spatial variations in the vertical profile of reflectivity. The two main recommendations for future work consist of adapting the rain separation method for radar network operations and documenting Z–R relationships conditional on rainfall type.

scholarly journals Model-based iterative approach to polarimetric radar rainfall estimation in presence of path attenuation

2005 ◽  
Vol 2 ◽  
pp. 51-57 ◽  
Author(s):  
G. Vulpiani ◽  
F. S. Marzano ◽  
V. Chandrasekar ◽  
R. Uijlenhoet
Keyword(s):  
Rain Rate ◽  
Radar Data ◽  
Phase Constraint ◽  
Solution Technique ◽  
Radar Rainfall ◽  
One Dimensional ◽  
Model Based ◽  
Raindrop Size Distribution ◽  
Radar Measurements ◽  
Raindrop Size

Abstract. A new model-based iterative technique to correct for attenuation and differential attenuation and retrieve rain rate, based on a neural-network scheme and a differential phase constraint, is presented. Numerical simulations are used to investigate the efficiency and accuracy of this approach named NIPPER. The simulator is based on a T-matrix solution technique, while precipitation is characterized with respect to shape, raindrop size distribution and orientation. A sensitivity analysis is performed in order to evaluate the expected errors of this method. The performance of the proposed methodology on radar measurements is evaluated by using one-dimensional Gaussian shaped rain cell models and synthetic radar data derived from disdrometer measurements. Numerical results are discussed in order to evaluate the robustness of the proposed technique.

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